from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	Tuple         =    {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Union[str, Any]     					=		'''marian'''
			__a    :					int     					=		['''past_key_values''']
			__a    :					Union[str, Any]     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_81_01 ,  lowerCAmelCase__=None ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=5_81_00 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=5_81_00 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=True ,  **lowerCAmelCase__ ,  )	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				decoder_vocab_size or vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							__lowercase					     =				share_encoder_decoder_embeddings
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
											__lowercase					     =				OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]		)

											if self.use_past:
															__lowercase					     =				{0: '''batch'''}
															__lowercase					     =				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
															__lowercase					     =				{0: '''batch''', 1: '''decoder_sequence'''}
															__lowercase					     =				{0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase__ ,  direction='''inputs'''		)
							elif self.task == "causal-lm":
											# TODO: figure this case out.
											__lowercase					     =				OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]		)
											if self.use_past:
															__lowercase     ,	__lowercase					     =				self.num_layers
															for i in range(lowerCAmelCase__		):
																			__lowercase					     =				{0: '''batch''', 2: '''past_sequence + sequence'''}
																			__lowercase					     =				{0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
											__lowercase					     =				OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
											        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
											    ]		)

							return common_inputs





			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
											__lowercase					     =				super().outputs
							else:
											__lowercase					     =				super(lowerCAmelCase__ ,  self		).outputs
											if self.use_past:
															__lowercase     ,	__lowercase					     =				self.num_layers
															for i in range(lowerCAmelCase__		):
																			__lowercase					     =				{0: '''batch''', 2: '''past_sequence + sequence'''}
																			__lowercase					     =				{0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  )	->   Mapping[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self._generate_dummy_inputs_for_encoder_and_decoder(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							# Generate decoder inputs
							__lowercase					     =				seq_length if not self.use_past else 1
							__lowercase					     =				self._generate_dummy_inputs_for_encoder_and_decoder(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
							__lowercase					     =				dict(**lowerCAmelCase__ ,  **lowerCAmelCase__		)

							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
											else:
															import torch
											__lowercase     ,	__lowercase					     =				common_inputs['''input_ids'''].shape
											__lowercase					     =				common_inputs['''decoder_input_ids'''].shape[1]
											__lowercase     ,	__lowercase					     =				self.num_attention_heads
											__lowercase					     =				(
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__lowercase					     =				decoder_seq_length + 3
											__lowercase					     =				(
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											__lowercase					     =				torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase__ ,  lowerCAmelCase__		)] ,  dim=1		)

											__lowercase					     =				[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__lowercase     ,	__lowercase					     =				self.num_layers
											__lowercase					     =				min(lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				max(lowerCAmelCase__ ,  lowerCAmelCase__		) - min_num_layers
											__lowercase					     =				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

											for _ in range(lowerCAmelCase__		):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(lowerCAmelCase__		),
															        torch.zeros(lowerCAmelCase__		),
															        torch.zeros(lowerCAmelCase__		),
															        torch.zeros(lowerCAmelCase__		),
															    )		)
											# TODO: test this.
											__lowercase					     =				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
											for _ in range(lowerCAmelCase__ ,  lowerCAmelCase__		):
															common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__		), torch.zeros(lowerCAmelCase__		))		)
							return common_inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  )	->   Mapping[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self._generate_dummy_inputs_for_encoder_and_decoder(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
											else:
															import torch
											__lowercase     ,	__lowercase					     =				common_inputs['''input_ids'''].shape
											# Not using the same length for past_key_values
											__lowercase					     =				seqlen + 2
											__lowercase     ,	__lowercase					     =				self.num_layers
											__lowercase     ,	__lowercase					     =				self.num_attention_heads
											__lowercase					     =				(
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											__lowercase					     =				common_inputs['''attention_mask'''].dtype
											__lowercase					     =				torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__ ,  lowerCAmelCase__ ,  dtype=lowerCAmelCase__		)] ,  dim=1		)
											__lowercase					     =				[
											    (torch.zeros(lowerCAmelCase__		), torch.zeros(lowerCAmelCase__		)) for _ in range(lowerCAmelCase__		)
											]
							return common_inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  )	->   Mapping[str, Any]:

							'''simple docstring'''


							__lowercase					     =				compute_effective_axis_dimension(
							    lowerCAmelCase__ ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0		)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							__lowercase					     =				tokenizer.num_special_tokens_to_add(lowerCAmelCase__		)
							__lowercase					     =				compute_effective_axis_dimension(
							    lowerCAmelCase__ ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=lowerCAmelCase__		)

							# Generate dummy inputs according to compute batch and sequence
							__lowercase					     =				[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
							__lowercase					     =				dict(tokenizer(lowerCAmelCase__ ,  return_tensors=lowerCAmelCase__		)		)
							return common_inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = -1 ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  )	->   Mapping[str, Any]:

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
											__lowercase					     =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  seq_length=lowerCAmelCase__ ,  is_pair=lowerCAmelCase__ ,  framework=lowerCAmelCase__		)

							else:
											__lowercase					     =				self._generate_dummy_inputs_for_causal_lm(
											    lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  seq_length=lowerCAmelCase__ ,  is_pair=lowerCAmelCase__ ,  framework=lowerCAmelCase__		)

							return common_inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
											__lowercase					     =				super()._flatten_past_key_values_(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							else:
											__lowercase					     =				super(lowerCAmelCase__ ,  self		)._flatten_past_key_values_(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   float:

							'''simple docstring'''


							return 1E-4
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings